Papers by Hanjie Zhao
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis (2025.coling-main)
Copied to clipboard
Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
| Challenge: | Currently, most sentiment analysis corpora use sequence-level annotation. |
| Approach: | They propose a two-stage approach to financial entity-level sentiment analysis called Self-aware In-context Learning Correction. |
| Outcome: | The proposed approach achieves state-of-the-art on the largest English and Chinese financial entity-level sentiment analysis datasets to date. |
MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios (2025.findings-acl)
Copied to clipboard
JinYang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, Libo Qin
| Challenge: | MLDebugging is a benchmark designed to assess debugging challenges within multi-library Python code. |
| Approach: | They propose to introduce a benchmark to assess debugging challenges within multi-library Python code using 126 Python libraries. |
| Outcome: | The proposed benchmark covers 126 Python libraries and a wide range of multi-library code issues. |
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for aspect-based sentiment analysis are limited and integrating with existing techniques is difficult. |
| Approach: | They propose a framework that utilizes in-context learning as a feature-aware mechanism that facilitates adaptive learning in multi-domain ABSA tasks. |
| Outcome: | The proposed framework achieves significant performance improvements in multiple domains compared to baselines, increasing F1 by 2.07% on average. |
GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels (2025.coling-main)
Copied to clipboard
| Challenge: | Existing literature on nested entity recognition is insufficient partly due to insufficient annotated data. |
| Approach: | They propose a method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. |
| Outcome: | The proposed method significantly enhances entity recognition, matching state-of-the-art (SOTA) models without additional training data. |
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training (2024.lrec-main)
Copied to clipboard
| Challenge: | Experimental evaluations conducted on the CMeEE, a benchmark for Chinese nested medical named entity recognition (NER) model outperforms the compared state-of-the-art (SOTA) models. |
| Approach: | They propose a model based on machine reading comprehension that uses a task-adaptive pre-training strategy to improve the model’s capability in the medical field. |
| Outcome: | The proposed model outperforms the compared state-of-the-art models on the CMeEE, a benchmark for Chinese nested medical NER. |